ArcGIS vs. Databricks Data Intelligence Platform

I'm very grateful to be able to use it, and I have a master's degree with a focus in Geospatial Analysis. There can be a bit of a learning curve, and I try to build user-friendly ways for volunteers to see & collect data. Meanwhile, if a colleague is less confident with building such a system, it may be more difficult for them to implement.

Incentivized

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

Incentivized

• Enterprise GIS - multi-user, multi-versioned geodatabase access through a host of apps and web tools really help people stay connected to data.
• Cartography - fantastic control and customization available with the ability to automate workflows.
• Training - tons of training videos, seminars, and other learning materials are easily accessed. The help documentation is superb.
• Ability to manage an organization through dashboards is extremely helpful.

Incentivized

• Process raw data in One Lake (S3) env to relational tables and views
• Share notebooks with our business analysts so that they can use the queries and generate value out of the data
• Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
• Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers

Incentivized

• There is a steep learning curve for ArcGIS users.
• It takes a lot of computing power for ArcGIS, as it can't be installed on every computer.
• At times, tools can be outdated and there are many similar competitors making more web-based tools that can replace ArcGIS.

Incentivized

• Sometimes, when multiple jobs depend on each other in different environments, it is not always easy to see the full workflow in one place.
• It is sometimes difficult to determine which job or cluster contributes more to the overall cost.
• For beginners, cluster configuration may be a little difficult. So more recommendation in the platform can help.

Incentivized

Simply because the program deserves it. It seems to me that it is a fundamental tool for the storage, analysis, and interpretation of medium and large-scale phenomena, unmanageable with traditional engineering software. Its versatility in the handling of the different "layers" with which the data is handled and interpolation tools, make this software a powerful ally both for companies and for the educational part of the universities.

Once set up, the tools are extremely easy to use. I had a staff member develop a tool for field data collection, that included an external and internal dashboards to monitor progress in days. The field workers that collected the data, barely knew how to use a computer, and within minutes they could use the application that was configured for them.

Incentivized

Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

Incentivized

Unlike other platforms (ex: EMSI), there is no "help desk" new users can easily call into for troubleshooting or errors, and so you have to spend LOTS of time trying workarounds. This is also because the help center blog posts are usually pretty confusing, and many times do not include images or videos to help you along. Any such changes would be immensely useful!

One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

My students love the "drop" feature in Google Maps, but besides that it truly doesn't compare. I love that you can add, delete, or change layers to this map to better understand its larger affect. There are many more ways to manipulate maps on ArcGIS than on Google Maps. I can also add personal details and information if I want to create a specific map, something that I am unable to do with Google

Incentivized

The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.

Incentivized

• ArcGIS saves us countless hours of manual paperwork by allowing us to conduct inspections in the field.
• Due to the high upfront costs, the ROI will definitely take longer than other competing products
• The PDF export feature allows us to share our maps with the public while maintaining control of the data associated with the map.

Incentivized

• The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
• DB has the ability to terminate/time out instances which helps manage cost.
• The ability to quickly access typical hard to build data scenarios easily is a strength.

Incentivized